{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目背景\n",
    "不吹牛集团这几年孵化了50个品牌，在各渠道做了大量品宣层面的曝光。现在集团首席吹牛官提了两个需求：\n",
    "\n",
    "1. 要一张大表，包含每个月搜索人数TOP5的品牌相关数据，以及对应品牌在当月的搜索份额和排名。\n",
    "\n",
    "2. 在现有数据基础上，找到最近一年投放效果还不错的品牌，要吹吹牛，做年度表彰。\n",
    "\n",
    "这是小z特别准备的两个具有代表性的需求：\n",
    "\n",
    "- 第一种：业务方已经定好了条条框框，需要数据分析师做的是取数和处理的工作，这样的“分析”工作，坑往往在于取数和清洗的复杂性。\n",
    "- 第二种：业务方自己想了个模糊的方向，需要分析师结合实际数据，定逻辑，给建议。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预览\n",
    "\n",
    "话音未落，集团首席吹牛官的跟屁虫就把相关源数据丢过来了\n",
    "\n",
    "一共24张Excel表格，按月存储，涵盖了从2019年1月到2020年12月的数据。\n",
    "\n",
    "表格内部数据大同小异："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看19年1月份的样例数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   品牌      50 non-null     object\n",
      " 1   品牌搜索人数  50 non-null     int64 \n",
      " 2   点击人数    50 non-null     int64 \n",
      " 3   支付人数    50 non-null     int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel('data/品牌投放/2019-01.xlsx')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>七喜</td>\n",
       "      <td>770</td>\n",
       "      <td>590</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>万迅</td>\n",
       "      <td>1369</td>\n",
       "      <td>948</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东方</td>\n",
       "      <td>1400</td>\n",
       "      <td>919</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>九方</td>\n",
       "      <td>955</td>\n",
       "      <td>531</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>佳禾</td>\n",
       "      <td>706</td>\n",
       "      <td>343</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品牌  品牌搜索人数  点击人数  支付人数\n",
       "0  七喜     770   590    22\n",
       "1  万迅    1369   948     0\n",
       "2  东方    1400   919     2\n",
       "3  九方     955   531     6\n",
       "4  佳禾     706   343     2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每张表都有50个品牌，包括了品牌名、品牌搜索人数、点击人数和对应的支付人数这几个关键字段。\n",
    "\n",
    "源数据就是这样简简单单中又透漏着麻麻烦烦，接下来，我们就开始手撕需求。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目一: 批量操作\n",
    "\n",
    "开始动手前，我们要明确需求。\n",
    "\n",
    "再回顾一下首席吹牛官的第一个需求：要一张大表，包含每个月搜索人数TOP5品牌的相关数据，以及对应品牌在当月的搜索份额和排名。\n",
    "\n",
    "提炼：在现有源数据的基础上，我们还需要对各品牌月内按搜索人数排序，然后计算每个品牌搜索份额，取其前5，最后遍历汇总。\n",
    "\n",
    "自动化之哥曾经说过：Python批量操作Excel，无论表格再多，处理逻辑再复杂，只要我们集中力量击破一张，就能够实现批量操作的全面胜利。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，我们要解决的是单张表的问题。\n",
    "### 1.1 按搜索人数排名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数\n",
       "14  同兴   41000  15519   519\n",
       "7   凌云   31482  19783  1627\n",
       "21  思优   30225  15804   502\n",
       "43  菊风   18662  12686   101\n",
       "22  恒聪   12525   8102    44"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values('品牌搜索人数',ascending = False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 加上排名列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名\n",
       "14  同兴   41000  15519   519     1.0\n",
       "7   凌云   31482  19783  1627     2.0\n",
       "21  思优   30225  15804   502     3.0\n",
       "43  菊风   18662  12686   101     4.0\n",
       "22  恒聪   12525   8102    44     5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['搜索人数排名'] = df['品牌搜索人数'].rank(ascending = False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 计算搜索份额指标\n",
    "\n",
    "再来计算搜索份额，搜索份额的计算公式：单品牌搜索人数/所有品牌搜索人数汇总，用Pandas计算，怎一个easy了的！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "      <th>搜索份额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.140092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.134498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.083044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.055735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名      搜索份额\n",
       "14  同兴   41000  15519   519     1.0  0.182446\n",
       "7   凌云   31482  19783  1627     2.0  0.140092\n",
       "21  思优   30225  15804   502     3.0  0.134498\n",
       "43  菊风   18662  12686   101     4.0  0.083044\n",
       "22  恒聪   12525   8102    44     5.0  0.055735"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['搜索份额'] = df['品牌搜索人数'] / df['品牌搜索人数'].sum()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 改了需求，重点关注凌云品牌\n",
    "正当我们准备批量执行操作，首席吹牛官发来了消息：\n",
    "\n",
    "“需求一略有调整，投资人最关注的是凌云这个品牌，要求在汇总表中，每个月凌云品牌的相关指标排在最前面，后面跟着搜索排名TOP5的品牌”。\n",
    "\n",
    "面对需求的临时改动，见过大风大浪的我们内心没有一丝波澜，甚至还有一点想笑。小事一桩，改改Pandas逻辑就好了。\n",
    "\n",
    "先找到目标品牌凌云："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "      <th>搜索份额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.140092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名      搜索份额\n",
       "7  凌云   31482  19783  1627     2.0  0.140092"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand = '凌云'\n",
    "brand_data = df.loc[df['品牌'].str.find(brand) != -1,:]\n",
    "brand_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再按照顺延的逻辑，定位TOP5品牌相关数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "      <th>搜索份额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.134498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.083044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.055735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>银嘉</td>\n",
       "      <td>8759</td>\n",
       "      <td>5968</td>\n",
       "      <td>41</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.038977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名      搜索份额\n",
       "14  同兴   41000  15519   519     1.0  0.182446\n",
       "21  思优   30225  15804   502     3.0  0.134498\n",
       "43  菊风   18662  12686   101     4.0  0.083044\n",
       "22  恒聪   12525   8102    44     5.0  0.055735\n",
       "47  银嘉    8759   5968    41     6.0  0.038977"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other = df.loc[df['品牌'].str.find(brand) == -1,:]\n",
    "other_top5 = other.iloc[:5,:]\n",
    "other_top5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合并即可获取我们想要的结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "      <th>搜索份额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.140092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.134498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.083044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.055735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>银嘉</td>\n",
       "      <td>8759</td>\n",
       "      <td>5968</td>\n",
       "      <td>41</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.038977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名      搜索份额\n",
       "7   凌云   31482  19783  1627     2.0  0.140092\n",
       "14  同兴   41000  15519   519     1.0  0.182446\n",
       "21  思优   30225  15804   502     3.0  0.134498\n",
       "43  菊风   18662  12686   101     4.0  0.083044\n",
       "22  恒聪   12525   8102    44     5.0  0.055735\n",
       "47  银嘉    8759   5968    41     6.0  0.038977"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([brand_data,other_top5])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单表操作完成，批量操作，只需要建立好循环+合并关系，并引入日期列，在合并结果中对不同的表数据做好区分："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 循环执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索人数排名</th>\n",
       "      <th>搜索份额</th>\n",
       "      <th>日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>凌云</td>\n",
       "      <td>31482</td>\n",
       "      <td>19783</td>\n",
       "      <td>1627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.140092</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>同兴</td>\n",
       "      <td>41000</td>\n",
       "      <td>15519</td>\n",
       "      <td>519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182446</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>思优</td>\n",
       "      <td>30225</td>\n",
       "      <td>15804</td>\n",
       "      <td>502</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.134498</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>菊风</td>\n",
       "      <td>18662</td>\n",
       "      <td>12686</td>\n",
       "      <td>101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.083044</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>恒聪</td>\n",
       "      <td>12525</td>\n",
       "      <td>8102</td>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.055735</td>\n",
       "      <td>2019-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌  品牌搜索人数   点击人数  支付人数  搜索人数排名      搜索份额       日期\n",
       "7   凌云   31482  19783  1627     2.0  0.140092  2019-01\n",
       "14  同兴   41000  15519   519     1.0  0.182446  2019-01\n",
       "21  思优   30225  15804   502     3.0  0.134498  2019-01\n",
       "43  菊风   18662  12686   101     4.0  0.083044  2019-01\n",
       "22  恒聪   12525   8102    44     5.0  0.055735  2019-01"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list = []\n",
    "\n",
    "for name in os.listdir('data/品牌投放/'):\n",
    "    df = pd.read_excel(f'data/品牌投放/{name}')\n",
    "    df = df.sort_values('品牌搜索人数',ascending = False)\n",
    "    df['搜索人数排名'] = df['品牌搜索人数'].rank(ascending = False)\n",
    "    df['搜索份额'] = df['品牌搜索人数'] / df['品牌搜索人数'].sum()\n",
    "    \n",
    "    brand = '凌云'\n",
    "    brand_data = df.loc[df['品牌'].str.find(brand) != -1,:]\n",
    "    \n",
    "    other = df.loc[df['品牌'].str.find(brand) == -1,:]\n",
    "    other_top5 = other.iloc[:5,:]\n",
    "    data = pd.concat([brand_data,other_top5])\n",
    "    data['日期'] = name[:-5]\n",
    "    \n",
    "    data_list.append(data)\n",
    "\n",
    "result = pd.concat(data_list)\n",
    "result.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas批量操作，就是如此丝滑~\n",
    "\n",
    "第一个需求搞定。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目二: 品牌投放分析\n",
    "\n",
    "还记得那个明（che）确（dan）的需求二吗？\n",
    "“在现有数据基础上，找到最近一年投放效果还不错的品牌，要吹吹牛，做年度表彰。”\n",
    "\n",
    "首席吹牛官以成本数据过于机密为由，除了说各品牌费用基本无差别之外，没有透露任何关于成本方面的数据，我们自然也无法计算投放ROI了这些核心指标了。\n",
    "\n",
    "目前能够拿到的，只有品牌、搜索人数、点击人数和对应支付人数这几个指标。\n",
    "\n",
    "要找到最近一年投放效果还不错的品牌，我们可以用漏斗思维，从量级（人数）和效率（转化率）两个角度来考虑：\n",
    "\n",
    "在费用无差别的情况下：\n",
    "\n",
    "- 人群基数大（搜索人数），表示投放的心智效果不错，让更多用户被广告触达后，在平台主动搜相关的品牌。\n",
    "- 搜索-点击转化率高，代表了搜索结果的精准度，搜索后展示页面的吸引力等等\n",
    "- 点击-支付转化率高，更可能受产品详情页面、活动力度等影响\n",
    "\n",
    "在项目二场景中，三个指标越高越好。接下来，我们就结合搜索人数，搜索-点击转化率和点击-支付转化率，用Pandas做一波分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 筛选出2020年订单\n",
    "\n",
    "要对最近一年的数据做分析，我们先把2020年所有数据合并，拿到汇总表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据行数：600\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>七喜</td>\n",
       "      <td>96885</td>\n",
       "      <td>46896</td>\n",
       "      <td>4692</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>万迅</td>\n",
       "      <td>30070</td>\n",
       "      <td>21386</td>\n",
       "      <td>4393</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东方</td>\n",
       "      <td>354060</td>\n",
       "      <td>72224</td>\n",
       "      <td>7544</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>九方</td>\n",
       "      <td>244846</td>\n",
       "      <td>103363</td>\n",
       "      <td>17097</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>佳禾</td>\n",
       "      <td>6547</td>\n",
       "      <td>3257</td>\n",
       "      <td>337</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品牌  品牌搜索人数    点击人数   支付人数       日期\n",
       "0  七喜   96885   46896   4692  2020-01\n",
       "1  万迅   30070   21386   4393  2020-01\n",
       "2  东方  354060   72224   7544  2020-01\n",
       "3  九方  244846  103363  17097  2020-01\n",
       "4  佳禾    6547    3257    337  2020-01"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list = []\n",
    "for name in os.listdir('data/品牌投放/'):\n",
    "    df = pd.read_excel(f'data/品牌投放/{name}')\n",
    "    df['日期'] = name[:-5]\n",
    "    data_list.append(df)\n",
    "    \n",
    "final = pd.concat(data_list)\n",
    "final_last = final.loc[final['日期'].str.find('2020') != -1,:]\n",
    "\n",
    "print('数据行数：{}'.format(len(final_last)))\n",
    "final_last.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2.2 提取关键字段，按品牌分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>双敏</td>\n",
       "      <td>1604198</td>\n",
       "      <td>571399</td>\n",
       "      <td>61244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>九方</td>\n",
       "      <td>1552916</td>\n",
       "      <td>712139</td>\n",
       "      <td>101217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>巨奥</td>\n",
       "      <td>1417267</td>\n",
       "      <td>512847</td>\n",
       "      <td>45790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>商软</td>\n",
       "      <td>1002679</td>\n",
       "      <td>544392</td>\n",
       "      <td>72050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>戴硕</td>\n",
       "      <td>920350</td>\n",
       "      <td>540284</td>\n",
       "      <td>30371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌   品牌搜索人数    点击人数    支付人数\n",
       "12  双敏  1604198  571399   61244\n",
       "3   九方  1552916  712139  101217\n",
       "20  巨奥  1417267  512847   45790\n",
       "15  商软  1002679  544392   72050\n",
       "24  戴硕   920350  540284   30371"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp = final_last.groupby('品牌')[['品牌搜索人数','点击人数','支付人数']].sum().reset_index()\n",
    "gp = gp.sort_values('品牌搜索人数',ascending = False)\n",
    "gp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 计算关键字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌</th>\n",
       "      <th>品牌搜索人数</th>\n",
       "      <th>点击人数</th>\n",
       "      <th>支付人数</th>\n",
       "      <th>搜索-点击转化率</th>\n",
       "      <th>点击-支付转化率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>双敏</td>\n",
       "      <td>1604198</td>\n",
       "      <td>571399</td>\n",
       "      <td>61244</td>\n",
       "      <td>0.356190</td>\n",
       "      <td>0.107183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>九方</td>\n",
       "      <td>1552916</td>\n",
       "      <td>712139</td>\n",
       "      <td>101217</td>\n",
       "      <td>0.458582</td>\n",
       "      <td>0.142131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>巨奥</td>\n",
       "      <td>1417267</td>\n",
       "      <td>512847</td>\n",
       "      <td>45790</td>\n",
       "      <td>0.361856</td>\n",
       "      <td>0.089286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>商软</td>\n",
       "      <td>1002679</td>\n",
       "      <td>544392</td>\n",
       "      <td>72050</td>\n",
       "      <td>0.542937</td>\n",
       "      <td>0.132349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>戴硕</td>\n",
       "      <td>920350</td>\n",
       "      <td>540284</td>\n",
       "      <td>30371</td>\n",
       "      <td>0.587042</td>\n",
       "      <td>0.056213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品牌   品牌搜索人数    点击人数    支付人数  搜索-点击转化率  点击-支付转化率\n",
       "12  双敏  1604198  571399   61244  0.356190  0.107183\n",
       "3   九方  1552916  712139  101217  0.458582  0.142131\n",
       "20  巨奥  1417267  512847   45790  0.361856  0.089286\n",
       "15  商软  1002679  544392   72050  0.542937  0.132349\n",
       "24  戴硕   920350  540284   30371  0.587042  0.056213"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp['搜索-点击转化率'] = gp['点击人数'] / gp['品牌搜索人数']\n",
    "gp['点击-支付转化率'] = gp['支付人数'] / gp['点击人数']\n",
    "gp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基础数据齐活了！\n",
    "\n",
    "从仅有的head5数据可以看到，双敏品牌以160万的搜索人数独占鳌头，但是！排名第二的九方，虽然搜索人数少了40多万，却能凭借较高的搜索-点击转化率和点击-支付转化率，在支付人数上远超双敏，成为支付之王。\n",
    "\n",
    "表格太晦涩，我们画个图吧：\n",
    "\n",
    "注：因为分析背景是无差别投放，搜索人数重要性非常高，为了可视化简洁清晰，我们简单粗暴的筛选TOP15品牌来绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 设置字体避免中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 TOP15搜索品牌图形绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 4000x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_data = gp.iloc[:15,:]\n",
    "\n",
    "my_dpi=80\n",
    "plt.figure(figsize=(800/my_dpi, 480/my_dpi), dpi=my_dpi * 5)\n",
    " \n",
    "x = draw_data['搜索-点击转化率'].to_list()\n",
    "y = draw_data['点击-支付转化率'].to_list()\n",
    "z = draw_data['品牌搜索人数']\n",
    "text = draw_data['品牌'].to_list()\n",
    "plt.scatter(x, y, s = z / 1000, c = x, cmap = \"Reds\", alpha = 0.7, edgecolors = \"grey\", linewidth = 1)\n",
    "\n",
    "for i,txt in enumerate(text):\n",
    "    plt.text(x=x[i], y=y[i], s=txt, size=11, horizontalalignment='center', verticalalignment='center')\n",
    "\n",
    "plt.xlabel(\"搜索-点击转化率\")\n",
    "plt.ylabel(\"点击-支付转化率\")\n",
    "plt.title(\"TOP15品牌搜索分布\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据气泡图，我们按照搜索-点击转化率和点击-支付转化率的高低划分了4个区间：\n",
    "\n",
    "区间1：高搜索-点击转化，高点击-支付转化\n",
    "\n",
    "区间2：低搜索-点击转化，高点击-支付转化\n",
    "\n",
    "区间3：低搜索-点击转化，低点击-支付转化\n",
    "\n",
    "区间4：高搜索-点击转化，低点击-支付转化\n",
    "\n",
    "再结合数据表，看的更加清晰："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_89d5a_row0_col0,#T_89d5a_row0_col1,#T_89d5a_row0_col2,#T_89d5a_row0_col3,#T_89d5a_row0_col4,#T_89d5a_row0_col5,#T_89d5a_row1_col0,#T_89d5a_row1_col1,#T_89d5a_row1_col2,#T_89d5a_row1_col3,#T_89d5a_row1_col4,#T_89d5a_row1_col5,#T_89d5a_row2_col0,#T_89d5a_row2_col1,#T_89d5a_row2_col2,#T_89d5a_row2_col3,#T_89d5a_row2_col4,#T_89d5a_row2_col5,#T_89d5a_row3_col0,#T_89d5a_row3_col1,#T_89d5a_row3_col2,#T_89d5a_row3_col3,#T_89d5a_row3_col4,#T_89d5a_row3_col5,#T_89d5a_row4_col0,#T_89d5a_row4_col1,#T_89d5a_row4_col2,#T_89d5a_row4_col3,#T_89d5a_row4_col4,#T_89d5a_row4_col5,#T_89d5a_row5_col0,#T_89d5a_row5_col1,#T_89d5a_row5_col2,#T_89d5a_row5_col3,#T_89d5a_row5_col4,#T_89d5a_row5_col5,#T_89d5a_row6_col0,#T_89d5a_row6_col1,#T_89d5a_row6_col2,#T_89d5a_row6_col3,#T_89d5a_row6_col4,#T_89d5a_row6_col5,#T_89d5a_row7_col0,#T_89d5a_row7_col1,#T_89d5a_row7_col2,#T_89d5a_row7_col3,#T_89d5a_row7_col4,#T_89d5a_row7_col5,#T_89d5a_row8_col0,#T_89d5a_row8_col1,#T_89d5a_row8_col2,#T_89d5a_row8_col3,#T_89d5a_row8_col4,#T_89d5a_row8_col5,#T_89d5a_row9_col0,#T_89d5a_row9_col1,#T_89d5a_row9_col2,#T_89d5a_row9_col3,#T_89d5a_row9_col4,#T_89d5a_row9_col5,#T_89d5a_row10_col0,#T_89d5a_row10_col1,#T_89d5a_row10_col2,#T_89d5a_row10_col3,#T_89d5a_row10_col4,#T_89d5a_row10_col5,#T_89d5a_row11_col0,#T_89d5a_row11_col1,#T_89d5a_row11_col2,#T_89d5a_row11_col3,#T_89d5a_row11_col4,#T_89d5a_row11_col5,#T_89d5a_row12_col0,#T_89d5a_row12_col1,#T_89d5a_row12_col2,#T_89d5a_row12_col3,#T_89d5a_row12_col4,#T_89d5a_row12_col5,#T_89d5a_row13_col0,#T_89d5a_row13_col1,#T_89d5a_row13_col2,#T_89d5a_row13_col3,#T_89d5a_row13_col4,#T_89d5a_row13_col5,#T_89d5a_row14_col0,#T_89d5a_row14_col1,#T_89d5a_row14_col2,#T_89d5a_row14_col3,#T_89d5a_row14_col4,#T_89d5a_row14_col5{\n",
       "            text-align:  center;\n",
       "        }</style><table id=\"T_89d5a_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >品牌</th>        <th class=\"col_heading level0 col1\" >品牌搜索人数</th>        <th class=\"col_heading level0 col2\" >点击人数</th>        <th class=\"col_heading level0 col3\" >支付人数</th>        <th class=\"col_heading level0 col4\" >搜索-点击转化率</th>        <th class=\"col_heading level0 col5\" >点击-支付转化率</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_89d5a_level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
       "                        <td id=\"T_89d5a_row0_col0\" class=\"data row0 col0\" >双敏</td>\n",
       "                        <td id=\"T_89d5a_row0_col1\" class=\"data row0 col1\" >1604198</td>\n",
       "                        <td id=\"T_89d5a_row0_col2\" class=\"data row0 col2\" >571399</td>\n",
       "                        <td id=\"T_89d5a_row0_col3\" class=\"data row0 col3\" >61244</td>\n",
       "                        <td id=\"T_89d5a_row0_col4\" class=\"data row0 col4\" >36%</td>\n",
       "                        <td id=\"T_89d5a_row0_col5\" class=\"data row0 col5\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "                        <td id=\"T_89d5a_row1_col0\" class=\"data row1 col0\" >九方</td>\n",
       "                        <td id=\"T_89d5a_row1_col1\" class=\"data row1 col1\" >1552916</td>\n",
       "                        <td id=\"T_89d5a_row1_col2\" class=\"data row1 col2\" >712139</td>\n",
       "                        <td id=\"T_89d5a_row1_col3\" class=\"data row1 col3\" >101217</td>\n",
       "                        <td id=\"T_89d5a_row1_col4\" class=\"data row1 col4\" >46%</td>\n",
       "                        <td id=\"T_89d5a_row1_col5\" class=\"data row1 col5\" >14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "                        <td id=\"T_89d5a_row2_col0\" class=\"data row2 col0\" >巨奥</td>\n",
       "                        <td id=\"T_89d5a_row2_col1\" class=\"data row2 col1\" >1417267</td>\n",
       "                        <td id=\"T_89d5a_row2_col2\" class=\"data row2 col2\" >512847</td>\n",
       "                        <td id=\"T_89d5a_row2_col3\" class=\"data row2 col3\" >45790</td>\n",
       "                        <td id=\"T_89d5a_row2_col4\" class=\"data row2 col4\" >36%</td>\n",
       "                        <td id=\"T_89d5a_row2_col5\" class=\"data row2 col5\" >9%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row3\" class=\"row_heading level0 row3\" >15</th>\n",
       "                        <td id=\"T_89d5a_row3_col0\" class=\"data row3 col0\" >商软</td>\n",
       "                        <td id=\"T_89d5a_row3_col1\" class=\"data row3 col1\" >1002679</td>\n",
       "                        <td id=\"T_89d5a_row3_col2\" class=\"data row3 col2\" >544392</td>\n",
       "                        <td id=\"T_89d5a_row3_col3\" class=\"data row3 col3\" >72050</td>\n",
       "                        <td id=\"T_89d5a_row3_col4\" class=\"data row3 col4\" >54%</td>\n",
       "                        <td id=\"T_89d5a_row3_col5\" class=\"data row3 col5\" >13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row4\" class=\"row_heading level0 row4\" >24</th>\n",
       "                        <td id=\"T_89d5a_row4_col0\" class=\"data row4 col0\" >戴硕</td>\n",
       "                        <td id=\"T_89d5a_row4_col1\" class=\"data row4 col1\" >920350</td>\n",
       "                        <td id=\"T_89d5a_row4_col2\" class=\"data row4 col2\" >540284</td>\n",
       "                        <td id=\"T_89d5a_row4_col3\" class=\"data row4 col3\" >30371</td>\n",
       "                        <td id=\"T_89d5a_row4_col4\" class=\"data row4 col4\" >59%</td>\n",
       "                        <td id=\"T_89d5a_row4_col5\" class=\"data row4 col5\" >6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row5\" class=\"row_heading level0 row5\" >35</th>\n",
       "                        <td id=\"T_89d5a_row5_col0\" class=\"data row5 col0\" >毕博</td>\n",
       "                        <td id=\"T_89d5a_row5_col1\" class=\"data row5 col1\" >835493</td>\n",
       "                        <td id=\"T_89d5a_row5_col2\" class=\"data row5 col2\" >263549</td>\n",
       "                        <td id=\"T_89d5a_row5_col3\" class=\"data row5 col3\" >23879</td>\n",
       "                        <td id=\"T_89d5a_row5_col4\" class=\"data row5 col4\" >32%</td>\n",
       "                        <td id=\"T_89d5a_row5_col5\" class=\"data row5 col5\" >9%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row6\" class=\"row_heading level0 row6\" >38</th>\n",
       "                        <td id=\"T_89d5a_row6_col0\" class=\"data row6 col0\" >盟新</td>\n",
       "                        <td id=\"T_89d5a_row6_col1\" class=\"data row6 col1\" >769771</td>\n",
       "                        <td id=\"T_89d5a_row6_col2\" class=\"data row6 col2\" >407238</td>\n",
       "                        <td id=\"T_89d5a_row6_col3\" class=\"data row6 col3\" >49607</td>\n",
       "                        <td id=\"T_89d5a_row6_col4\" class=\"data row6 col4\" >53%</td>\n",
       "                        <td id=\"T_89d5a_row6_col5\" class=\"data row6 col5\" >12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "                        <td id=\"T_89d5a_row7_col0\" class=\"data row7 col0\" >兰金</td>\n",
       "                        <td id=\"T_89d5a_row7_col1\" class=\"data row7 col1\" >705190</td>\n",
       "                        <td id=\"T_89d5a_row7_col2\" class=\"data row7 col2\" >258279</td>\n",
       "                        <td id=\"T_89d5a_row7_col3\" class=\"data row7 col3\" >19659</td>\n",
       "                        <td id=\"T_89d5a_row7_col4\" class=\"data row7 col4\" >37%</td>\n",
       "                        <td id=\"T_89d5a_row7_col5\" class=\"data row7 col5\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row8\" class=\"row_heading level0 row8\" >31</th>\n",
       "                        <td id=\"T_89d5a_row8_col0\" class=\"data row8 col0\" >昊嘉</td>\n",
       "                        <td id=\"T_89d5a_row8_col1\" class=\"data row8 col1\" >654808</td>\n",
       "                        <td id=\"T_89d5a_row8_col2\" class=\"data row8 col2\" >373391</td>\n",
       "                        <td id=\"T_89d5a_row8_col3\" class=\"data row8 col3\" >27486</td>\n",
       "                        <td id=\"T_89d5a_row8_col4\" class=\"data row8 col4\" >57%</td>\n",
       "                        <td id=\"T_89d5a_row8_col5\" class=\"data row8 col5\" >7%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row9\" class=\"row_heading level0 row9\" >45</th>\n",
       "                        <td id=\"T_89d5a_row9_col0\" class=\"data row9 col0\" >超艺</td>\n",
       "                        <td id=\"T_89d5a_row9_col1\" class=\"data row9 col1\" >640732</td>\n",
       "                        <td id=\"T_89d5a_row9_col2\" class=\"data row9 col2\" >306960</td>\n",
       "                        <td id=\"T_89d5a_row9_col3\" class=\"data row9 col3\" >50461</td>\n",
       "                        <td id=\"T_89d5a_row9_col4\" class=\"data row9 col4\" >48%</td>\n",
       "                        <td id=\"T_89d5a_row9_col5\" class=\"data row9 col5\" >16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row10\" class=\"row_heading level0 row10\" >16</th>\n",
       "                        <td id=\"T_89d5a_row10_col0\" class=\"data row10 col0\" >四通</td>\n",
       "                        <td id=\"T_89d5a_row10_col1\" class=\"data row10 col1\" >546822</td>\n",
       "                        <td id=\"T_89d5a_row10_col2\" class=\"data row10 col2\" >315530</td>\n",
       "                        <td id=\"T_89d5a_row10_col3\" class=\"data row10 col3\" >15172</td>\n",
       "                        <td id=\"T_89d5a_row10_col4\" class=\"data row10 col4\" >58%</td>\n",
       "                        <td id=\"T_89d5a_row10_col5\" class=\"data row10 col5\" >5%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row11\" class=\"row_heading level0 row11\" >5</th>\n",
       "                        <td id=\"T_89d5a_row11_col0\" class=\"data row11 col0\" >信诚</td>\n",
       "                        <td id=\"T_89d5a_row11_col1\" class=\"data row11 col1\" >498473</td>\n",
       "                        <td id=\"T_89d5a_row11_col2\" class=\"data row11 col2\" >303204</td>\n",
       "                        <td id=\"T_89d5a_row11_col3\" class=\"data row11 col3\" >57631</td>\n",
       "                        <td id=\"T_89d5a_row11_col4\" class=\"data row11 col4\" >61%</td>\n",
       "                        <td id=\"T_89d5a_row11_col5\" class=\"data row11 col5\" >19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row12\" class=\"row_heading level0 row12\" >49</th>\n",
       "                        <td id=\"T_89d5a_row12_col0\" class=\"data row12 col0\" >飞利</td>\n",
       "                        <td id=\"T_89d5a_row12_col1\" class=\"data row12 col1\" >495287</td>\n",
       "                        <td id=\"T_89d5a_row12_col2\" class=\"data row12 col2\" >232137</td>\n",
       "                        <td id=\"T_89d5a_row12_col3\" class=\"data row12 col3\" >25770</td>\n",
       "                        <td id=\"T_89d5a_row12_col4\" class=\"data row12 col4\" >47%</td>\n",
       "                        <td id=\"T_89d5a_row12_col5\" class=\"data row12 col5\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row13\" class=\"row_heading level0 row13\" >30</th>\n",
       "                        <td id=\"T_89d5a_row13_col0\" class=\"data row13 col0\" >昂歌</td>\n",
       "                        <td id=\"T_89d5a_row13_col1\" class=\"data row13 col1\" >473901</td>\n",
       "                        <td id=\"T_89d5a_row13_col2\" class=\"data row13 col2\" >252463</td>\n",
       "                        <td id=\"T_89d5a_row13_col3\" class=\"data row13 col3\" >19094</td>\n",
       "                        <td id=\"T_89d5a_row13_col4\" class=\"data row13 col4\" >53%</td>\n",
       "                        <td id=\"T_89d5a_row13_col5\" class=\"data row13 col5\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_89d5a_level0_row14\" class=\"row_heading level0 row14\" >27</th>\n",
       "                        <td id=\"T_89d5a_row14_col0\" class=\"data row14 col0\" >方正</td>\n",
       "                        <td id=\"T_89d5a_row14_col1\" class=\"data row14 col1\" >458785</td>\n",
       "                        <td id=\"T_89d5a_row14_col2\" class=\"data row14 col2\" >217636</td>\n",
       "                        <td id=\"T_89d5a_row14_col3\" class=\"data row14 col3\" >17563</td>\n",
       "                        <td id=\"T_89d5a_row14_col4\" class=\"data row14 col4\" >47%</td>\n",
       "                        <td id=\"T_89d5a_row14_col5\" class=\"data row14 col5\" >8%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
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       "<pandas.io.formats.style.Styler at 0x12c97d4e730>"
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       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,yellow 12.5%, transparent 12.5%);\n",
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       "            text-align:  center;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
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       "             height:  80%;\n",
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       "             height:  80%;\n",
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       "            text-align:  center;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,yellow 12.4%, transparent 12.4%);\n",
       "        }#T_160c0_row14_col4{\n",
       "            text-align:  center;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,yellow 78.0%, transparent 78.0%);\n",
       "        }#T_160c0_row14_col5{\n",
       "            text-align:  center;\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,yellow 13.3%, transparent 13.3%);\n",
       "        }</style><table id=\"T_160c0_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >品牌</th>        <th class=\"col_heading level0 col1\" >品牌搜索人数</th>        <th class=\"col_heading level0 col2\" >点击人数</th>        <th class=\"col_heading level0 col3\" >支付人数</th>        <th class=\"col_heading level0 col4\" >搜索-点击转化率</th>        <th class=\"col_heading level0 col5\" >点击-支付转化率</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_160c0_level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
       "                        <td id=\"T_160c0_row0_col0\" class=\"data row0 col0\" >双敏</td>\n",
       "                        <td id=\"T_160c0_row0_col1\" class=\"data row0 col1\" >1604198</td>\n",
       "                        <td id=\"T_160c0_row0_col2\" class=\"data row0 col2\" >571399</td>\n",
       "                        <td id=\"T_160c0_row0_col3\" class=\"data row0 col3\" >61244</td>\n",
       "                        <td id=\"T_160c0_row0_col4\" class=\"data row0 col4\" >36%</td>\n",
       "                        <td id=\"T_160c0_row0_col5\" class=\"data row0 col5\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "                        <td id=\"T_160c0_row1_col0\" class=\"data row1 col0\" >九方</td>\n",
       "                        <td id=\"T_160c0_row1_col1\" class=\"data row1 col1\" >1552916</td>\n",
       "                        <td id=\"T_160c0_row1_col2\" class=\"data row1 col2\" >712139</td>\n",
       "                        <td id=\"T_160c0_row1_col3\" class=\"data row1 col3\" >101217</td>\n",
       "                        <td id=\"T_160c0_row1_col4\" class=\"data row1 col4\" >46%</td>\n",
       "                        <td id=\"T_160c0_row1_col5\" class=\"data row1 col5\" >14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "                        <td id=\"T_160c0_row2_col0\" class=\"data row2 col0\" >巨奥</td>\n",
       "                        <td id=\"T_160c0_row2_col1\" class=\"data row2 col1\" >1417267</td>\n",
       "                        <td id=\"T_160c0_row2_col2\" class=\"data row2 col2\" >512847</td>\n",
       "                        <td id=\"T_160c0_row2_col3\" class=\"data row2 col3\" >45790</td>\n",
       "                        <td id=\"T_160c0_row2_col4\" class=\"data row2 col4\" >36%</td>\n",
       "                        <td id=\"T_160c0_row2_col5\" class=\"data row2 col5\" >9%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row3\" class=\"row_heading level0 row3\" >15</th>\n",
       "                        <td id=\"T_160c0_row3_col0\" class=\"data row3 col0\" >商软</td>\n",
       "                        <td id=\"T_160c0_row3_col1\" class=\"data row3 col1\" >1002679</td>\n",
       "                        <td id=\"T_160c0_row3_col2\" class=\"data row3 col2\" >544392</td>\n",
       "                        <td id=\"T_160c0_row3_col3\" class=\"data row3 col3\" >72050</td>\n",
       "                        <td id=\"T_160c0_row3_col4\" class=\"data row3 col4\" >54%</td>\n",
       "                        <td id=\"T_160c0_row3_col5\" class=\"data row3 col5\" >13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row4\" class=\"row_heading level0 row4\" >24</th>\n",
       "                        <td id=\"T_160c0_row4_col0\" class=\"data row4 col0\" >戴硕</td>\n",
       "                        <td id=\"T_160c0_row4_col1\" class=\"data row4 col1\" >920350</td>\n",
       "                        <td id=\"T_160c0_row4_col2\" class=\"data row4 col2\" >540284</td>\n",
       "                        <td id=\"T_160c0_row4_col3\" class=\"data row4 col3\" >30371</td>\n",
       "                        <td id=\"T_160c0_row4_col4\" class=\"data row4 col4\" >59%</td>\n",
       "                        <td id=\"T_160c0_row4_col5\" class=\"data row4 col5\" >6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row5\" class=\"row_heading level0 row5\" >35</th>\n",
       "                        <td id=\"T_160c0_row5_col0\" class=\"data row5 col0\" >毕博</td>\n",
       "                        <td id=\"T_160c0_row5_col1\" class=\"data row5 col1\" >835493</td>\n",
       "                        <td id=\"T_160c0_row5_col2\" class=\"data row5 col2\" >263549</td>\n",
       "                        <td id=\"T_160c0_row5_col3\" class=\"data row5 col3\" >23879</td>\n",
       "                        <td id=\"T_160c0_row5_col4\" class=\"data row5 col4\" >32%</td>\n",
       "                        <td id=\"T_160c0_row5_col5\" class=\"data row5 col5\" >9%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row6\" class=\"row_heading level0 row6\" >38</th>\n",
       "                        <td id=\"T_160c0_row6_col0\" class=\"data row6 col0\" >盟新</td>\n",
       "                        <td id=\"T_160c0_row6_col1\" class=\"data row6 col1\" >769771</td>\n",
       "                        <td id=\"T_160c0_row6_col2\" class=\"data row6 col2\" >407238</td>\n",
       "                        <td id=\"T_160c0_row6_col3\" class=\"data row6 col3\" >49607</td>\n",
       "                        <td id=\"T_160c0_row6_col4\" class=\"data row6 col4\" >53%</td>\n",
       "                        <td id=\"T_160c0_row6_col5\" class=\"data row6 col5\" >12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "                        <td id=\"T_160c0_row7_col0\" class=\"data row7 col0\" >兰金</td>\n",
       "                        <td id=\"T_160c0_row7_col1\" class=\"data row7 col1\" >705190</td>\n",
       "                        <td id=\"T_160c0_row7_col2\" class=\"data row7 col2\" >258279</td>\n",
       "                        <td id=\"T_160c0_row7_col3\" class=\"data row7 col3\" >19659</td>\n",
       "                        <td id=\"T_160c0_row7_col4\" class=\"data row7 col4\" >37%</td>\n",
       "                        <td id=\"T_160c0_row7_col5\" class=\"data row7 col5\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row8\" class=\"row_heading level0 row8\" >31</th>\n",
       "                        <td id=\"T_160c0_row8_col0\" class=\"data row8 col0\" >昊嘉</td>\n",
       "                        <td id=\"T_160c0_row8_col1\" class=\"data row8 col1\" >654808</td>\n",
       "                        <td id=\"T_160c0_row8_col2\" class=\"data row8 col2\" >373391</td>\n",
       "                        <td id=\"T_160c0_row8_col3\" class=\"data row8 col3\" >27486</td>\n",
       "                        <td id=\"T_160c0_row8_col4\" class=\"data row8 col4\" >57%</td>\n",
       "                        <td id=\"T_160c0_row8_col5\" class=\"data row8 col5\" >7%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row9\" class=\"row_heading level0 row9\" >45</th>\n",
       "                        <td id=\"T_160c0_row9_col0\" class=\"data row9 col0\" >超艺</td>\n",
       "                        <td id=\"T_160c0_row9_col1\" class=\"data row9 col1\" >640732</td>\n",
       "                        <td id=\"T_160c0_row9_col2\" class=\"data row9 col2\" >306960</td>\n",
       "                        <td id=\"T_160c0_row9_col3\" class=\"data row9 col3\" >50461</td>\n",
       "                        <td id=\"T_160c0_row9_col4\" class=\"data row9 col4\" >48%</td>\n",
       "                        <td id=\"T_160c0_row9_col5\" class=\"data row9 col5\" >16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row10\" class=\"row_heading level0 row10\" >16</th>\n",
       "                        <td id=\"T_160c0_row10_col0\" class=\"data row10 col0\" >四通</td>\n",
       "                        <td id=\"T_160c0_row10_col1\" class=\"data row10 col1\" >546822</td>\n",
       "                        <td id=\"T_160c0_row10_col2\" class=\"data row10 col2\" >315530</td>\n",
       "                        <td id=\"T_160c0_row10_col3\" class=\"data row10 col3\" >15172</td>\n",
       "                        <td id=\"T_160c0_row10_col4\" class=\"data row10 col4\" >58%</td>\n",
       "                        <td id=\"T_160c0_row10_col5\" class=\"data row10 col5\" >5%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row11\" class=\"row_heading level0 row11\" >5</th>\n",
       "                        <td id=\"T_160c0_row11_col0\" class=\"data row11 col0\" >信诚</td>\n",
       "                        <td id=\"T_160c0_row11_col1\" class=\"data row11 col1\" >498473</td>\n",
       "                        <td id=\"T_160c0_row11_col2\" class=\"data row11 col2\" >303204</td>\n",
       "                        <td id=\"T_160c0_row11_col3\" class=\"data row11 col3\" >57631</td>\n",
       "                        <td id=\"T_160c0_row11_col4\" class=\"data row11 col4\" >61%</td>\n",
       "                        <td id=\"T_160c0_row11_col5\" class=\"data row11 col5\" >19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row12\" class=\"row_heading level0 row12\" >49</th>\n",
       "                        <td id=\"T_160c0_row12_col0\" class=\"data row12 col0\" >飞利</td>\n",
       "                        <td id=\"T_160c0_row12_col1\" class=\"data row12 col1\" >495287</td>\n",
       "                        <td id=\"T_160c0_row12_col2\" class=\"data row12 col2\" >232137</td>\n",
       "                        <td id=\"T_160c0_row12_col3\" class=\"data row12 col3\" >25770</td>\n",
       "                        <td id=\"T_160c0_row12_col4\" class=\"data row12 col4\" >47%</td>\n",
       "                        <td id=\"T_160c0_row12_col5\" class=\"data row12 col5\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row13\" class=\"row_heading level0 row13\" >30</th>\n",
       "                        <td id=\"T_160c0_row13_col0\" class=\"data row13 col0\" >昂歌</td>\n",
       "                        <td id=\"T_160c0_row13_col1\" class=\"data row13 col1\" >473901</td>\n",
       "                        <td id=\"T_160c0_row13_col2\" class=\"data row13 col2\" >252463</td>\n",
       "                        <td id=\"T_160c0_row13_col3\" class=\"data row13 col3\" >19094</td>\n",
       "                        <td id=\"T_160c0_row13_col4\" class=\"data row13 col4\" >53%</td>\n",
       "                        <td id=\"T_160c0_row13_col5\" class=\"data row13 col5\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_160c0_level0_row14\" class=\"row_heading level0 row14\" >27</th>\n",
       "                        <td id=\"T_160c0_row14_col0\" class=\"data row14 col0\" >方正</td>\n",
       "                        <td id=\"T_160c0_row14_col1\" class=\"data row14 col1\" >458785</td>\n",
       "                        <td id=\"T_160c0_row14_col2\" class=\"data row14 col2\" >217636</td>\n",
       "                        <td id=\"T_160c0_row14_col3\" class=\"data row14 col3\" >17563</td>\n",
       "                        <td id=\"T_160c0_row14_col4\" class=\"data row14 col4\" >47%</td>\n",
       "                        <td id=\"T_160c0_row14_col5\" class=\"data row14 col5\" >8%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c97c0e970>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
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    }
   ],
   "source": [
    "max_v = draw_data[['搜索-点击转化率', '点击-支付转化率']].max().max()\n",
    "\n",
    "(\n",
    "    draw_data.style\n",
    "    .set_properties(**{'text-align': 'center'})\n",
    "    .format({'搜索-点击转化率':\"{:.0%}\", '点击-支付转化率': \"{:.0%}\"})\n",
    "    .bar(subset=['搜索-点击转化率', '点击-支付转化率'], vmin=0, vmax=max_v, color='yellow')\n",
    ")"
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    "结果显而易见，高搜索量级的品牌，主要呈现出两种形态：\n",
    "\n",
    "以双敏（排名第1）、巨奥（排名第3）为代表的品牌主要分布在第三区间，量级较大，但两种转化效率均需要进一步提升，品牌没能较好的承接蜂拥而至的流量。\n",
    "\n",
    "九方（排名第2）、商软（排名第4）则是高搜索量级、高转化效率的代表，从现有数据看，他们才是不吹牛集团学习的榜样。\n",
    "\n",
    "正当我们准备把这一步结果同步给首席吹牛官，顺便探讨进一步的数据分析方向，比如结合支付人数的金额贡献、留存率、LTV，以及引入两年增速的维度，结合业务动作来定位深层原因。\n",
    "\n",
    "没想到首席吹牛官发来了这样的消息：\n",
    "\n",
    "“第二个需求我可能没说清楚，这次不仅是表彰，也是给融资机构秀肌肉的一部分，我们关注的只是品牌声量，对应的就是品牌搜索人数这个指标，你汇总好排个序就好”\n",
    "\n",
    "我们每个人会说超过5种语言的脏话，但在这个场景，大部分人只能条件反射般的打出这8个字：\n",
    "\n",
    "> “嗯嗯，好的，马上给到”"
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